Bottom Line:
Previous studies have provided methods for inferring information diffusion networks in which each node corresponds to an individual person.However, in the case of disease transmission, to effectively propose and implement intervention strategies, it is more realistic and reasonable for policy makers to study the diffusion patterns at a metapopulation level when the disease transmission is affected by mobile population, that is, to consider disease transmission networks in which nodes represent subpopulations, and links indicate their interrelationships.In addition, it also discloses some hidden phenomenon.

Background: To investigate transmission patterns of an infectious disease, e.g., malaria, it is desirable to use the observed surveillance data to discover the underlying (often hidden) disease transmission networks. Previous studies have provided methods for inferring information diffusion networks in which each node corresponds to an individual person. However, in the case of disease transmission, to effectively propose and implement intervention strategies, it is more realistic and reasonable for policy makers to study the diffusion patterns at a metapopulation level when the disease transmission is affected by mobile population, that is, to consider disease transmission networks in which nodes represent subpopulations, and links indicate their interrelationships.

Results: A network inference method called NetEpi (Network Epidemic) is developed and evaluated using both synthetic and real-world datasets. The experimental results show that NetEpi can not only recover most of the ground-truth disease transmission networks using only surveillance data, but also find a malaria transmission network based on a real-world dataset. The inferred malaria network can characterize the real-world observations to a certain extent. In addition, it also discloses some hidden phenomenon.

Conclusions: This research addresses the problem of inferring disease transmission networks at a metapopulation level. Such networks can be useful in several ways: (i) to investigate hidden impact factors that influence epidemic dynamics, (ii) to reveal possible sources of epidemic outbreaks, and (iii) to practically develop and/or improve strategies for controlling the spread of infectious diseases.

Fig7: Differences between the edge number in the inferred networks and the ground-truth networks. For each dataset index, we take the average of all the networks with different topologies but same size. The network size increases as the index increases. (A) - (C) show the results of core-periphery networks, hierarchical community networks, and random graphs respectively. It is obvious that as the ground-truth network size increases, the accuracy of NetEpi decreases. The number of false edges increases as well. This results from the increased number of possible combinations of neighbors for each node to achieve its global optimal solution.

Mentions:
For networks with the same topologies but a different number of nodes, NetEpi achieves better results when inferring smaller networks, as shown in Figure 6. At the beginning of the inference process, no edge information is given. Therefore, a ground-truth network is treated as a complete network. Even given its approximate structure Gp, the complexity quadratically increases as the number of nodes increases. Meanwhile, as the edge number increases, the number of neighborhood combinations needed for each node to achieve an optimal solution also increases, which directly interferes the inference results, as shown in Figure 7.Figure 7

Fig7: Differences between the edge number in the inferred networks and the ground-truth networks. For each dataset index, we take the average of all the networks with different topologies but same size. The network size increases as the index increases. (A) - (C) show the results of core-periphery networks, hierarchical community networks, and random graphs respectively. It is obvious that as the ground-truth network size increases, the accuracy of NetEpi decreases. The number of false edges increases as well. This results from the increased number of possible combinations of neighbors for each node to achieve its global optimal solution.

Mentions:
For networks with the same topologies but a different number of nodes, NetEpi achieves better results when inferring smaller networks, as shown in Figure 6. At the beginning of the inference process, no edge information is given. Therefore, a ground-truth network is treated as a complete network. Even given its approximate structure Gp, the complexity quadratically increases as the number of nodes increases. Meanwhile, as the edge number increases, the number of neighborhood combinations needed for each node to achieve an optimal solution also increases, which directly interferes the inference results, as shown in Figure 7.Figure 7

Bottom Line:
Previous studies have provided methods for inferring information diffusion networks in which each node corresponds to an individual person.However, in the case of disease transmission, to effectively propose and implement intervention strategies, it is more realistic and reasonable for policy makers to study the diffusion patterns at a metapopulation level when the disease transmission is affected by mobile population, that is, to consider disease transmission networks in which nodes represent subpopulations, and links indicate their interrelationships.In addition, it also discloses some hidden phenomenon.

Background: To investigate transmission patterns of an infectious disease, e.g., malaria, it is desirable to use the observed surveillance data to discover the underlying (often hidden) disease transmission networks. Previous studies have provided methods for inferring information diffusion networks in which each node corresponds to an individual person. However, in the case of disease transmission, to effectively propose and implement intervention strategies, it is more realistic and reasonable for policy makers to study the diffusion patterns at a metapopulation level when the disease transmission is affected by mobile population, that is, to consider disease transmission networks in which nodes represent subpopulations, and links indicate their interrelationships.

Results: A network inference method called NetEpi (Network Epidemic) is developed and evaluated using both synthetic and real-world datasets. The experimental results show that NetEpi can not only recover most of the ground-truth disease transmission networks using only surveillance data, but also find a malaria transmission network based on a real-world dataset. The inferred malaria network can characterize the real-world observations to a certain extent. In addition, it also discloses some hidden phenomenon.

Conclusions: This research addresses the problem of inferring disease transmission networks at a metapopulation level. Such networks can be useful in several ways: (i) to investigate hidden impact factors that influence epidemic dynamics, (ii) to reveal possible sources of epidemic outbreaks, and (iii) to practically develop and/or improve strategies for controlling the spread of infectious diseases.